Select Individuals (Non-Residential)
We can one of the visualization methods we learned to examine the
tracks for non-residential (migratory) behavior.
ggplot(data=elk_gps, aes(x=lon, y=lat)) +
geom_path(size=0.5) +
theme_classic() +
facet_wrap(~id, scale="free", ncol=3)
We can select some of the individuals with clear distinctions between
residential and transiting behavior (movement “blob” followed by a
straight dispersal line and then a second movement “blob”).
elk_mig <- elk_gps |>
subset(id %in%
c("YL96", "GP2", "YL25", "YL29", "YL73", "YL77", "YL78")) |>
mutate(id = droplevels(id))
We can visualize our selected tracks, looking at the change in
latitude over time. Each track has a clear residential period in
latitude, followed by a drop in latitude, a second residential period,
and for most, a second spike in latitude as they disperse back to the
first area of residency.
ggplot(data=elk_mig, aes(x=datetime, y=lat)) +
geom_path(size=0.5) +
theme_classic() +
facet_wrap(~id, scale="free", ncol=2)
Regularizing data with AniMotum
Note: You can also jump directly to the next section if
regularization is not a big deal.
To properly fit a hidden Markov model, the data should ideally be
regularized - i.e. the time intervals should be consistent.
A useful and very fast package for track regularization in R is
AniMotum. This package itself actually uses a fast-fitting
state space model framework (similar to an HMM but taking into account
locational error) to predict locations in regular time along a
trajectory.
You can read more about this package with the published paper, Jonsen
et al 2023 and with the AniMotum
OVerview Vignette.
You will need to install the package using the following code and
ensuring that the latest versions of the TMB and
Matrix R packages are also installed.
# install.packages("aniMotum",
# repos = c("https://cloud.r-project.org",
# "https://ianjonsen.r-universe.dev"),
# dependencies = TRUE)
# install.packages("Matrix")
# install.packages("TMB")
library(aniMotum)
For use with the aniMotum package, your data needs to have the
following exact column names, order, and structure:
id, date (POSIXct), lc,
lon, lat and (optional) additional locational
error information (e.g., smaj, smin,
eor columns that come with Argos satellite tag data -
reflecting the marine biology background of these tools).
Since our data is from GPS collars, we will the class “G” option for
the lc column. We’re going to perform this analysis
wiht
elk_mig2 <- elk_mig |>
mutate(lc = "G", date = datetime) |>
dplyr::select(id, date, lc, lon, lat)
We now need to choose a sampling rate to predict our locations
to.
This should be informed by the most prominent fix rates in your data.
You can check this using the following code (see also in lab 1):
dtime <- function(t, ...) {difftime(t[-1], t[-length(t)], ...) %>% as.numeric}
require(dplyr)
sample.rate <- elk_mig2 |>
group_by(id) |>arrange(date) |>
summarize(dtime = round(dtime(date, units ="hours")))
table(sample.rate$dtime)
##
## 0 1 2 3 4 5 6 7 8 9 10 12 14
## 14815 8246 13910 51 943 4 229 1 81 1 36 23 8
## 16 18 20 22 24 26 98 133
## 8 4 2 2 1 1 1 1
plot(table(sample.rate$dtime))
There are a few (very few) enormous gaps, and many that are less than
1/2 hour, but 2 hours seems like a good mode.
We can now fit the predictive model to our data, using the
fit_ssm function and additional arguments, such as the
maximum speed (“vmax”, in m/s, and above which locations will be flagged
as outliers), the desired time step (“time.step”, in hours), and the
model type (“rw” for random walk and “crw” for correlated random
walk).
We will use a reasonable max speed cut off of 20 m/s, a random walk
model (works better for large gaps), add a time step of 2 hours.
Note - this step can take a long time! So we focus only on our friend
“YL96” and also absolutely save this fit
myelk <- elk_mig2 |> subset(id == "YL96")
myelk_ssm_fit <- fit_ssm(myelk,
vmax = 20,
model = "crw",
time.step = 2,
control = ssm_control(verbose = 0))
load("./data/myelk_ssm_fit.rda")
myelk_reg <- elk_mig2 |> subset(id == "YL96")
We can use the summary function to look at the
individual-level model results, including whether the models converged
and AIC values.
summary(myelk_ssm_fit)
## Animal id Model Time n.obs n.filt n.fit n.pred n.rr converged AICc
## YL96 crw 2 4129 0 4129 2422 . TRUE 13303.7
##
## --------------
## YL96
## --------------
## Parameter Estimate Std.Err
## D_x 0.0468 0.0022
## D_y 0.0465 0.0022
## rho_p -0.167 0.0323
## rho_o -0.1258 0.039
## tau_x 1.9146 0.0536
## tau_y 1.856 0.0522
This is a “model” of the animal’s movement that also allows you to
“predict” the locations.
We can define a function, extractPredictions, to extract
the predicted lat/lon coordinates for our data.
extractPredictions <- function(ssm_fit){
predictions <- data.frame(ssm_fit$ssm[[1]]$predicted) |>
sf::st_as_sf() |>
sf::st_transform(4326)
coords <- sf::st_coordinates(predictions)
predictions$lon <- coords[, 1]
predictions$lat <- coords[, 2]
new_data <- predictions |>
data.frame() |>
dplyr::select(id, date, lon, lat)
return(new_data)
}
We can apply the function to list of fitted models for each
individual.
myelk_regdata <- extractPredictions(myelk_ssm_fit)
The predicted locations can be visualized over the original
locations, using the ggplot function and storing the plots within a
for-loop to print the results for each individual.
Let’s compare the regular data with the original data:
myelk <- subset(elk_gps, id == "YL96") |> dplyr::mutate(id = as.character(id))
plot(lon~datetime, type = "o", pch = 19, data = myelk[1:100,])
lines(lon~date, type = "o", pch = 4, col = 2, data = myelk_regdata[1:100,])
legend("topright", legend = c("original", "regular"), pch = c(19,4), col = 1:2)
plot(lon~lat, type = "o", pch = 19, data = myelk[1:100,])
lines(lon~lat, type = "o", pch = 4, col = 2, data = myelk_regdata[1:100,])
legend("topright", legend = c("original", "regular"), pch = c(19,4), col = 1:2)
We went ahead and performed this operation for many of those animals
and saved them to a single “regularized” elk data frame which you can
load:
load(file="./data/elk_regdata.rda")
Hidden Markov Model
Hidden Markov Models (HMMs) can be used for time series data to
describe 2 processes, one observed (e.g., the data produced by
telemetry, such as locations and movement metrics) and one “hidden”,
representing the “hidden” ecological behaviors driving the observed
data.
The Markov chain portion of HMM’s state that the probability of being
in a particular behavior state at time \(t+1\) is only dependent on
the current state at time \(t\). Once
these probabilities are described, a transition probability matrix, or
the probability of moving between behavior states, can be described.
HMM’s can have any number of hidden behavioral states, granted that they
are biologically meaningful (model selection can also assist with
determining the “optimal” number of states).
Importantly, HMMs are performed in discrete time and assume that
observations are “conditionally independent”. These can be difficult for
movement data derived from tags, which can be prone to irregularly
spaced observations and errors.
Load the data
Now that our data is regular space/time, we can use the methods we
learned in lab 2 to make our data spatial, convert the coordinates to a
projected coordinate system, and save the X/Y coordinates as columns in
our data.
library(sf)
Prepare
load(file="./data/elk_regdata.rda")
myelk_utm <- elk_regdata |> subset(id == "YL96") |>
st_as_sf(coords = c("lon","lat"), crs = 4326) |>
st_transform(32611)
We will need the coordinates! But the data should be a data frame
myelk_utm <- cbind(myelk_utm, st_coordinates(myelk_utm)) |> data.frame()
head(myelk_utm)
## id date X Y geometry
## 1 YL96 2004-03-29 17:00:00 599661.1 5733368 POINT (599661.1 5733368)
## 3 YL96 2004-03-29 19:00:00 597878.1 5734876 POINT (597878.1 5734876)
## 5 YL96 2004-03-29 21:00:00 597157.1 5732873 POINT (597157.1 5732873)
## 7 YL96 2004-03-29 23:00:00 596874.3 5732420 POINT (596874.3 5732420)
## 9 YL96 2004-03-30 01:00:00 596492.0 5732126 POINT (596492 5732126)
## 11 YL96 2004-03-30 03:00:00 596745.6 5732220 POINT (596745.6 5732220)
Fit HMM: Two States
Now that our data is regularized and we have our movement metrics
annotated to our data, we can fit our HMM with the
momentuHMM package, which is very flexible. You can use
user-specified parameter distributions, options for hierarchical and
multivariate models, biased correlated random walks and more. As usual,
read the vignette.
library(momentuHMM)
We first prepare our data using the prepData function to
grab our X/Y coordinates.
myelk_hmm_prep <- prepData(myelk_utm |> data.frame(), type = "UTM",
coordNames = c("X","Y"))
head(myelk_hmm_prep)
## ID step angle id date
## 1 Animal1 2335.3893 NA YL96 2004-03-29 17:00:00
## 2 Animal1 2129.0133 1.92742231 YL96 2004-03-29 19:00:00
## 3 Animal1 533.8463 -0.21297386 YL96 2004-03-29 21:00:00
## 4 Animal1 482.5415 -0.35596210 YL96 2004-03-29 23:00:00
## 5 Animal1 270.6406 2.84203213 YL96 2004-03-30 01:00:00
## 6 Animal1 1300.5587 -0.08190792 YL96 2004-03-30 03:00:00
## geometry x y
## 1 POINT (599661.1 5733368) 599661.1 5733368
## 2 POINT (597878.1 5734876) 597878.1 5734876
## 3 POINT (597157.1 5732873) 597157.1 5732873
## 4 POINT (596874.3 5732420) 596874.3 5732420
## 5 POINT (596492 5732126) 596492.0 5732126
## 6 POINT (596745.6 5732220) 596745.6 5732220
Note that once again - this package made a data frame that computes
all the things we computed, and ltraj computes.
It is important that step lengths be greater than 0:
table(myelk_hmm_prep$step == 0)
##
## FALSE
## 2421
MomentuHMM fits HMM’s within a Bayesian framework, where prior
distributions can be specified for each state-specific parameter to help
distinguish the “true” behavioral state when combined with observed
distributions.
We will start with a two-state model with state-specific and
distribution specific parameters for each variable that will be used to
distinguish our behavioral states, namely the step length and relative
turning angles.
We will make our priors somewhat informative, assuming that one state
will be defined by smaller step lengths (smaller mean and sd) and bigger
turning angles (smaller concentration parameter), with the opposite for
the second state.
stepMean0 <- c(m1 = 100, m2 = 4000)
stepSD0 <- c(sd1 = 50, sd2 = 1000)
angleCon0 <- c(rho1 = 0.1, rho2 = 0.8)
We will now assign names to our states, with the slower, more
tortuous state being defined as a “resident” state and the faster,
straighter state as a “transit” state.
stateNames <- c("resident","transit")
We now pick distributions for our variables of interest (step length
and turning angle), using a Gamma distribution for the step lengths
(?dgamma) and a Wrapped Cauchy distribution for the turning
angles (dwrpcauchy).
We store our priors defined in the code chunk above into a list
object, “Par0”.
dist <- list(step = "gamma", angle = "wrpcauchy")
Par0 <- list(step=c(stepMean0, stepSD0), angle = c(angleCon0))
We can now fit our HMM using the fitHMM function with
our prepped data and objects from above. We also specify the number of
states to identify (“nbStates”).
myelk_hmm_fit <- fitHMM(data = myelk_hmm_prep, nbStates = 2, dist = dist,
Par0 = Par0, stateNames = stateNames)
print(myelk_hmm_fit)
## Value of the maximum log-likelihood: -20243.62
##
##
## step parameters:
## ----------------
## resident transit
## mean 128.1196 672.7869
## sd 104.3391 550.0276
##
## angle parameters:
## -----------------
## resident transit
## mean 0.0000000 0.0000000
## concentration 0.1966093 0.6733011
##
## Regression coeffs for the transition probabilities:
## ---------------------------------------------------
## 1 -> 2 2 -> 1
## (Intercept) -1.271814 -0.8936583
##
## Transition probability matrix:
## ------------------------------
## resident transit
## resident 0.7810531 0.2189469
## transit 0.2903555 0.7096445
##
## Initial distribution:
## ---------------------
## resident transit
## 1.941931e-06 9.999981e-01
After fitting our model, we can use the Viterbi formula with the
viterbi function to decode the transition probabilities for
the most likely states at each time point along our movement
trajectory.
hmm_states <- viterbi(myelk_hmm_fit)
str(hmm_states)
## int [1:2422] 2 2 2 2 2 2 2 2 2 2 ...
We can add our predicted states as a column for each observation to
our data:
myelk_utm$state <- hmm_states
Plot 2-state predictions
The default plotting of momentuhmm is quite useful:
## Decoding state sequence... DONE
## plot(myelk_hmm_fit)
We can then plot the results, using Base R to make multidimensional
plots of the trajectory with the annotated states:
layout(cbind(c(1,1),2:3))
par(bty = "l", mar = c(2,2,2,2))
with(myelk_utm, {
plot(X, Y, asp =1, col = c("orange","blue")[state], pch = 19, cex = 0.7)
segments(X[-length(X)], Y[-length(Y)],
X[-1], Y[-1], col = c("orange","blue")[state[-length(state)]])
plot(date, X, col = c("orange","blue")[state], pch = 19, cex = 0.7)
segments(date[-length(X)], Y[-length(Y)],
date[-1], Y[-1], col = c("orange","blue")[state[-length(state)]])
plot(date, Y, col = c("orange","blue")[state], pch = 19, cex = 0.7)
segments(date[-length(X)], Y[-length(Y)],
date[-1], Y[-1], col = c("orange","blue")[state[-length(state)]])
})
We can also convert our data to an sf structure and use the methods
we learned in lab 1 to plot the trajectory with each location colored by
the predicted state with the mapview package.
library(mapview)
myelk_sf <- myelk_utm |>
st_as_sf(coords=c("X","Y"), crs= 32611) |>
st_transform(4326) |>
mutate(state = as.character(state))
myelk_track <- myelk_sf |>
summarize(do_union=FALSE) |>
st_cast("LINESTRING")
mapview(myelk_track, color="darkgrey") +
mapview(myelk_sf, zcol="state", col.regions=c("orange","blue"))
We can see from the plot that our movement “blobs” still have a
mixture of both colors. We can try fitting a multivariate HMM, with
latitude as a covariate, to assist with further separating our two
states along our movement track.
This may suggest that there are actually 3 behavioral states here:
one that is fast and straight (blue state), one that is slower and
tortuous (orange state), and a third that is intermediate, where the
animal is having directed, faster movements within its residential
patch.
Fit HMM: 3-State Model
We can fit a 3 state model by simply adding an additional
state-specific prior for each movement variable.
stepMean0 <- c(m1 = 50, m2 = 2000, m3 = 200)
stepSD0 <- c(sd1 = 50, sd2 = 1000, sd3 = 100)
angleCon0 <- c(rho1 = 0.1, rho2 = 0.8, rho3 = 0.2)
We will label this third state as an additional “faster” residential
state.
stateNames <- c("resident-slow","transit", "resident-faster")
dist <- list(step = "gamma", angle = "wrpcauchy")
Par0 <- list(step=c(stepMean0, stepSD0), angle = c(angleCon0))
We re-fit the model as before, but this time specifying 3 states.
myelk_hmm_threestate <- fitHMM(data = myelk_hmm_prep, nbStates = 3,
dist = dist, Par0 = Par0,
stateNames = stateNames,
formula = ~1)
myelk_hmm_threestate
## Value of the maximum log-likelihood: -20092.3
##
##
## step parameters:
## ----------------
## resident-slow transit resident-faster
## mean 102.18266 2093.955 466.2835
## sd 78.50872 1045.551 307.2804
##
## angle parameters:
## -----------------
## resident-slow transit resident-faster
## mean 0.0000000 0.0000000 0.000000
## concentration 0.1555151 0.7988473 0.622267
##
## Regression coeffs for the transition probabilities:
## ---------------------------------------------------
## 1 -> 2 1 -> 3 2 -> 1 2 -> 3 3 -> 1 3 -> 2
## (Intercept) -71.45947 -0.956771 -54.68058 0.04184668 -0.8096533 -2.582002
##
## Transition probability matrix:
## ------------------------------
## resident-slow transit resident-faster
## resident-slow 7.224748e-01 6.673727e-32 0.2775252
## transit 8.756147e-25 4.895399e-01 0.5104601
## resident-faster 2.926491e-01 4.973083e-02 0.6576201
##
## Initial distribution:
## ---------------------
## resident-slow transit resident-faster
## 4.214104e-06 9.999958e-01 2.303663e-08
We can decode our model results and bind the predicted states for
each observation back to our data:
hmm_3states <- viterbi(myelk_hmm_threestate)
myelk_utm$state <- hmm_3states
Plot 3-sate predictions
Now if we plot the results with our new state in green, we can see
that our model did a much better job at finding our transiting state and
two residential states within our movement “blobs”!
## Decoding state sequence... DONE
## plot(myelk_hmm_fit)
Here is a mapview:
myelk_track <- myelk_sf |>
summarize(do_union=FALSE) |>
st_cast("LINESTRING")
myelk_sf <- myelk_sf |> mutate(state = hmm_3states)
mapview(myelk_track, color="darkgrey") +
mapview(myelk_sf, zcol="state", col.regions=c("orange","blue", "green"))